Universitat Internacional de Catalunya
MÓDULO 6: Técnicas de Visualización de Datos
Other languages of instruction: Catalan, English
Teaching staff
Introduction
The large amount of data at our disposal makes visualization essential for the three processes involved in any Big Data project. First of all, to know the characteristics of the available data, then to analyze the results of the transformations of this data and finally to efficiently communicate these results.
In this course we will learn the different fields included in data visualization, we will learn how to build diagrams, graphics that are specific for Big Data, dynamic and interactive graphics, but above all, we will learn how to evaluate the graphics and to find the most appropriate one for each need.
Pre-course requirements
It is convenient but not essential to have some programming knowledge.
Objectives
To build static, dynamic and interactive graphics with the grammar of graphs.
To develop data visualization projects.
To evaluate a diagram and to find the optimal one if it exists.
To help oneself with the data visualization to understand a problem, to propose the solution, to supervise the results and finally to communicate this solution.
Learning outcomes of the subject
At the end of the course the student will be able to propose a graphic for each need, will be able to program with R the code to obtain the searched graphic, will be able to elaborate visualization projects and especially will improve the performance in all the processes involved in Big Data projects.
Syllabus
Dynamic report generation
- The origins; Markdown, Pandoc and R Markdown; Implementation in R
Theory of data graphics
- The origins; The elements of graphing data; Types of data graphics; Purpose of data graphics; Characterization of the variables; Chart selection methods
The grammar of graphics
- The origins; Layers
- Geometries; Coordinate systems; Transformations; Aesthetic attributes; Scales; Annotations; Themes
Graphics for Big Data using techniques based on:
- Geometric transformations; Pixels; Hierarchies; Icons
Interactive and dynamic graphics for data analysis
- The origins; Task by Data Type Taxonomy (TTT); Interactive techniques; Networks; Virtual reality and augmented reality
Data visualization projects
Teaching and learning activities
In person
Classes are face-to-face as long as the health situation allows it. The first session is fundamentally practical and the rest of the sessions are theoretical-practical. The exercises carried out in class will be complemented with the reading of scientific articles.
Evaluation systems and criteria
In person
The evaluation system will consist of three exercises. The first exercise will consist of the evaluation of exploratory graphics, the second will evaluate analysis graphics and the third will evaluate communication graphics.
Bibliography and resources
Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2019. Rmarkdown: Dynamic Documents for R. https://rmarkdown.rstudio.com.
Bertin, J. 1967. Sémiologie Graphique. Les Diagrammes, Les Réseaux, Les Cartes. Paris: Mouton.
Friendly, Michael. 2007. “HE Plots for Multivariate General Linear Models.” Journal of Computational and Graphical Statistics 16 (4): 421–44.
Healey, Christopher G. 1996. “Effective Visualization of Large Multidimensional Datasets.”
Millán-Martı́nez, Pere, and Pedro Valero-Mora. 2018. “Automating Statistical Diagrammatic Representations with Data Characterization.” Information Visualization 17 (4): 316–34.
Sarkar, Deepayan. 2008. Lattice: Multivariate Data Visualization with R. New York: Springer. http://lmdvr.r-forge.r-project.org.
Shneiderman, Ben. 1996. “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations.” In Proceedings of the 1996 Ieee Symposium on Visual Languages, 336. VL ’96. Washington, DC, USA: IEEE Computer Society.
Swayne, Deborah F., Dianne Cook, and Andreas Buja. 1998. “XGobi: Interactive Dynamic Data Visualization in the X Window System.” Journal of Computational and Graphical Statistics 7: 113–30.
Unwin, Antony, Martin Theus, and Heike Hofmann. 2006. Graphics of Large Datasets: Visualizing a Million (Statistics and Computing). Berlin, Heidelberg: Springer-Verlag.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Wilke, C. O. 2019. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media. https://serialmentor.com/dataviz/.
Wilkinson, L. 2005. The Grammar of Graphics. 2nd ed. Statistics and Computing. Springer.
Xie, Y. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Chapman & Hall/Crc the R Series. CRC Press. https://bookdown.org/yihui/bookdown/.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC.